José Luis Guzmán

Simulation of Greenhouse Climate Monitoring and Control with Wireless Sensor Network and Event-Based Control

Monitoring and control of the greenhouse environment play a decisive role in greenhouse production processes. Assurance of optimal climate conditions has a direct influence on crop growth performance, but it usually increases the required equipment cost. Traditionally, greenhouse installations have required a great effort to connect and distribute all the sensors and data acquisition systems. These installations need many data and power wires to be distributed along the greenhouses, making the system complex and expensive. For this reason, and others such as unavailability of distributed actuators, only individual sensors are usually located in a fixed point that is selected as representative of the overall greenhouse dynamics. On the other hand, the actuation system in greenhouses is usually composed by mechanical devices controlled by relays, being desirable to reduce the number of commutations of the control signals from security and economical point of views. Therefore, and in order to face these drawbacks, this paper describes how the greenhouse climate control can be represented as an event-based system in combination with wireless sensor networks, where low-frequency dynamics variables have to be controlled and control actions are mainly calculated against events produced by external disturbances. The proposed control system allows saving costs related with wear minimization and prolonging the actuator life, but keeping promising performance results. Analysis and conclusions are given by means of simulation results.

Interactive teaching of constrained generalized predictive control

In this paper, an interactive educational tool for teaching the basic concepts of constrained generalized predictive control is described. Students gain insight into the selection of design parameters and discover how controlled system performance and closed-loop stability are influenced by this selection through a user-friendly presentation of constrained and unconstrained cases.

Interactive learning modules for PID control [Lecture Notes]

This article describes three interactive learning modules that are designed to develop intuition as well as a working knowledge of proportional-integral-derivative (PID) control. These three modules comprise a package called interactive learning modules for PID (ILM-PID). By illustrating concepts such as tuning, robustness, loop shaping, and antiwindup, ILM-PID can be used for demonstrations, exercises, and self-study. In this article we use interactive graphics to learn PID control and develop intuition.

Dynamic model of microalgal production in tubular photobioreactors.

A dynamic model for microalgal culture is presented. The model takes into account the fluid-dynamic and mass transfer, in addition to biological phenomena, it being based on fundamental principles. The model has been calibrated and validated using data from a pilot-scale tubular photobioreactor but it can be extended to other designs. It can be used to determine, from experimental measurements, the values of characteristic parameters. The model also allows a simulation of the systems dynamic behaviour in response to solar radiation, making it a useful tool for design and operation optimization of photobioreactors. Moreover, the model permits the identification of local pH gradients, dissolved oxygen and dissolved carbon dioxide; that can damage microalgae growth. In addition, the developed model can map the different characteristic time scales of phenomena inside microalgae cultures within tubular photobioreactors, meaning it is a valuable tool in the development of advanced control strategies for microalgae cultures.

Multiobjective hierarchical control architecture for greenhouse crop growth

The problem of determining the trajectories to control greenhouse crop growth has traditionally been solved by using constrained optimization or applying artificial intelligence techniques. The economic profit has been used as the main criterion in most research on optimization to obtain adequate climatic control setpoints for the crop growth. This paper addresses the problem of greenhouse crop growth through a hierarchical control architecture governed by a high-level multiobjective optimization approach, where the solution to this problem is to find reference trajectories for diurnal and nocturnal temperatures (climate-related setpoints) and electrical conductivity (fertirrigation-related setpoints). The objectives are to maximize profit, fruit quality, and water-use efficiency, these being currently fostered by international rules. Illustrative results selected from those obtained in an industrial greenhouse during the last eight years are shown and described.

Generalized Predictive Control With Actuator Deadband for Event-Based Approaches

This work presents an event-based control structure using the generalized predictive control (GPC) algorithm with actuator deadband. The main objective of this work is to limit the number of controlled system updates. In this approach, the controlled process is sampled with a constant sampling time and is updated in an asynchronous way that depends on the obtained control signal value. To achieve the desired control properties, a hybrid system framework is used to model the virtual actuator deadband. The deadband is modeled as system input constraints in the GPCs optimization procedure, exploring mixed integer quadratic programming (MIQP) techniques. The presented control structure considers adjustable actuator deadband as an additional tuning parameter and allows a tradeoff between control performance and the number of events/updates. This fact is very important for the lifetime of the actuators and minimizes their wear, especially for mechanical and electromechanical systems. The minimization of actuator usage can be also understood as an economical saving. Other benefits can be found in distributed control systems, where various control system agents are connected through computer networks, and where a reduction in communication is always welcome.

Robust Nonlinear Predictive Control Applied to a Solar Collector Field in a Solar Desalination Plant

This brief presents the application of a robust nonlinear predictive controller to the distributed collector field of a solar desalination plant. The main purpose of the controller is to manipulate the water flow rate to maintain the collector outlet-inlet temperature gradient constant in spite of disturbances. The controller uses a robust dead-time compensation structure and a nonlinear model predictive control to cope with time delay uncertainties and system nonlinearities, respectively. Simulation and real experimental results are shown to illustrate controller performance.

Adaptive Control for a Mobile Robot Under Slip Conditions Using an LMI-Based Approach

This paper presents the synthesis of a control law which guarantees asymptotic stability for a wheeled mobile robot under slip conditions subject to both constraints and system dynamics. This control law is obtained using Linear Matrix Inequalities. The improvements provided by the pro- posed adaptive control are compared with other control laws, dealing with slip effect, through simulation.

Effective utilization of flue gases in raceway reactor with event-based pH control for microalgae culture

This work addresses effective utilization of flue gases through the proper pH control in raceway reactors. The pH control problem has been addressed with an event-based control approach using a Generalized Predictive Controller (GPC) with actuator deadband. Applying this control strategy it is possible to reduce the control effort, and at the same time saving control resources. In the pH process case, the event-based controller with actuator deadband can be tuned to supply only necessary amount of CO2 to keep the pH close to its optimal value. On the other hand, the evaluated control algorithm significantly improves the pH control accuracy, what has a direct influence on biomass production. In order to test the performance of the event-based GPC controller, several experiments have been performed on a real raceway reactor. Additionally, several control performance indexes have been used to compare the analyzed technique with commonly used on/off controller.

Online robust tube-based MPC for time-varying systems: a practical approach

This article focuses on the design of a robust model predictive control law for constrained discrete-time time-varying systems with additive uncertainties. The proposed solution to the control problem is a tube-based MPC ensuring robustness and constraints fulfilment. Reachable sets are calculated online taking into account the system dynamics by means of an adaptive local control law and additive uncertainties. The proposed method represents a trade-off between small conservativeness and efficient real-time execution. This approach is applied to solve the trajectory tracking problem of a mobile robot. Simulation results provide a comparison between the tube-based MPC scheme and established motion control algorithms, showing the efficient execution and satisfactory behaviour of the proposed controller.